In general, \(D\)  can be understood as a learnable loss function, where the realistic features we want to generate are implicit in the back-propagation, and \(G\)  gets better because of \(D\) 's gradient flows [1]}. This builds a need for \(D\)  to be a competitive feature extractor, so that the better the features extracted by \(D\) , the better \(G\)  can capture reality. The use of auxiliary classifying labels in \(D\)  is generally helpful (Sec. ). Additionally, multi-task setups can boost generative modeling performance, where multiple factors of the signal are predicted at the output of the generative model to enforce modeling better perceptual qualities of the signal [2]}. This is applicable to the SEGAN setup where the discriminator only learns features that lead to a real or fake decision, but is not concerned about specific factors of the signal that are important to remark the reconstructed speaker identity, prosody, or contents. Note that we can also have some additional regularization loss in the output of \(G\)  too, aggregated to the adversarial loss coming from \(D\) . For instance the first version of SEGAN used an \(L_1\)  regularization term that made \(G\)  output a zero-centered signal [3]}, and the posterior WSEGAN implementation substituted it by a less restrictive power loss [2]}, [5]}, enforcing proper energy allocation in the frequency bands.
